Hybrid Retrieval Approaches to Geospatial Music Recommendation Markus Schedl Dominik Schnitzer

First Page		Document Content
Date: 2013-04-18 01:28:58 Information science Information retrieval Software Recommender systems Music information retrieval Music software Natural language processing Collaborative filtering Last.fm		Hybrid Retrieval Approaches to Geospatial Music Recommendation Markus Schedl Dominik Schnitzer Add to Reading List Source URL: www.cp.jku.at Download Document from Source Website File Size: 309,20 KB Share Document on Facebook

	Next-Term Student Performance Prediction: A Recommender Systems Approach∗ Mack Sweeney Computer Science George Mason University DocID: 1vgeP - View Document
	Schema-summarization in Linked-Data-based feature selection for recommender systems Azzurra Ragone1 , Paolo Tomeo2 , Corrado Magarelli2 , Tommaso Di Noia2 , Matteo Palmonari1 Andrea Maurino1 , Eugenio Di Sciascio2 1 1 DocID: 1uPCf - View Document
	Personalisation and Recommender Systems in Digital Libraries Joint NSF-EU DELOS Working Group Report May 2003 Jamie Callan, Carnegie Mellon University, USA Alan Smeaton, Dublin City University, Ireland DocID: 1tUcx - View Document
	Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems Qingyun Wu, Hongning Wang Liangjie Hong∗ DocID: 1tJRw - View Document
	Journal of Machine Learning Research656 Submitted 3/08; Revised 11/08; Published 3/09 Scalable Collaborative Filtering Approaches for Large Recommender Systems DocID: 1tkme - View Document